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Verifiable and Forward-Secure Multi-Keyword Query in Internet of Medical Things

IEEE Internet of Things Journal 2025年第13期12卷 23809-23822页

作者： Li, Hongjun He, Debiao Feng, Qi Luo, Min Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan China Wuhan University School of Cyber Science and Engineering Wuhan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China National Key Laboratory of Security Communication Chengdu China

The Internet of Medical Things (IoMT) plays a pivotal role in modern healthcare systems, enhancing patients' medical experiences and improving the efficiency of public medical services. However, concerns regarding security and privacy may hinder the widespread implementation and development of IoMT in practical applications. Dynamic Searchable Symmetric Encryption (DSSE) can maintain search capabilities on encrypted data, even when files are dynamically added or deleted. Earlier DSSE schemes typically support only single keyword query and provide forward security under the assumption of semi-honest servers, which significantly limits their applicability in real-world scenarios. To resolve these limitations, we propose a forward-secure and verifiable DSSE scheme that supports multi-keyword conjunctive query. Our scheme ensures forward security by utilizing a chain structure and guarantees correctness and completeness through RSA accumulator and Homomorphic Message Authentication Code (HMAC). Furthermore, we integrate the forward index and inverted index to enable efficient retrieval. Through comprehensive security and performance analysis, we demonstrate that our scheme effectively protects users' privacy while maintaining low computation and communication overheads. Finally, some experimental evaluations are conducted to verify both the correctness and efficiency of the proposed scheme. © 2014 IEEE.

关键词： Structured Query Language

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High-Lift Prediction Workshop 5: High-Order Discretization Technology Focus Group Summary

High-Lift Prediction Workshop 5: High-Order Discretization T...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Galbraith, Marshall C. Ruiz-Gironés, Eloi Roca, Xevi Wang, Z.J. Holst, Kevin R. Skaperdas, Vangelis Department of Aeronautics and Astronautics Massachusetts Institute of Technology CambridgeMA02139 United States Applications in Science and Engineering Barcelona Supercomputing Center - BSC Barcelona Spain Department of Aerospace Engineering University of Kansas LawrenceKS66045 United States Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States Customer Service BETA CAE Systems Thessaloniki Greece

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper summarizes the High-Order Technical Focus Group (HO-TFG) submissions forthe 5th AIAA High-Lift Prediction Workshop (HLPW-5). The goal of the workshop was to assess the state-of-the-art in mesh generation and computational fluid dynamics software for predicting peak lift of transport aircraft with flaps and slats deployed. Numerous geometric build ups of the Common Research Model High-Lift (CRM-HL) variant and Reynolds number considerations served as the primary focus of the workshop, and a simple wing-body variant of the CRM-HL was used as a verification test case. The HO-TFG was tasked with generating high-order curved meshes for these complex geometries, and computing high-order solutions using both Reynolds Averaged Navier-Stokes (RANS) and scale resolving Large Eddy Simulation(LES) formulations. While participation in the HO-TFG was limited, the effort produced curved meshes on highly-complex geometric configurations and computational results from two participants. The LES results demonstrate the potential for producing results comparable to 2nd-order accurate methods, but with fewer degrees of freedom. The RANS submission for this workshop does not demonstrate a benefit over 2nd-order schemes. However, the authors will argue a lack of understanding for optimal mesh distributions for high-order methods is the cause for the limited benefits observed for the RANS results. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Reynolds number

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A Novel Transformer-based Multiscale Siamese Framework for High-resolution Remote Sensing Change Detection

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 13087-13099页

作者： Wang, Liangjun Chen, Weitao Wang, Haoyi Chen, Zhengchao China University of Geosciences Faculty of Computer Science Wuhan430074 China Ministry of Education Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Wuhan430074 China Airborne Remote Sensing Center Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

Change detection (CD) in remote sensing based on deep convolutional neural networks (CNNs) and transformers has played a crucial role in surface monitoring and resource development. However, the inadequate extraction of local and global features, along with the insufficient utilization of multiscale features, constrains the ability to identify change regions at multiple scales. To resolve the aforementioned issue, we propose a transformer-based multiscale Siamese framework (TMSF) for high-resolution remote sensing CD, aiming to improve detection accuracy. First, we propose a dual-attention token-based (DAT-based) transformer module, which embeds attention tokens into the transformer, effectively integrating local and global features and thereby enhancing the discriminative power of the extracted features. Second, we design a multiscale feature adaptive fusion module (MFAFM) that incorporates a channel attention mechanism to adaptively integrate multiscale information, enabling more precise identification of change regions at various scales. We verify the effectiveness of the proposed method in improving CD accuracy on three widely used benchmark datasets, LEVIR-CD, WHU-CD and CDD. Experimental results show that our method outperforms ten state-of-the-art CD methods in terms of F1-score and intersection over union (IoU) metrics. Notably, compared to that of five multiscale-based methods, our proposed TMSF achieves superior performance while requiring only half the number of parameters and computational cost. Thus, the proposed model demonstrates a marked advancement in remote sensing CD. © 2008-2012 IEEE.

关键词： Deep neural networks

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Dependency-Type Weighted Graph Convolutional Network on End-to-End Aspect-Based Sentiment Analysis 13th

Dependency-Type Weighted Graph Convolutional Network on End...

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13th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2024

作者： Mu, Yusong Shi, Shumin School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

ISBN: (纸本)9783031579189

Previous studies consider little on using dependency-type messages in the E2E-ABSA task. Studies using dependency-type messages just contact the dependency-type message and word embedding vectors, which may not fully fuse the context feature and information from the dependency type. This paper proposes a new model called Dependency-Type Weighted Graph Convolution Network (DTW-GCN) to compose dependency-type messages and word embedding. We use a type-weighted matrix to combine the dependency-type message, and DTW-GCN could fuse the dependency-type message and word embedding vectors. Experiments conducted on three benchmark datasets verify the effectiveness of our model. © IFIP International Federation for Information Processing 2024.

关键词： Sentiment analysis

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DRSC: Dual-Reweighted Siamese Contrastive Learning Network for Cross-Domain Rotating Machinery Fault Diagnosis With Multi-Source Domain Imbalanced Data

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IEEE Internet of Things Journal 2025年第13期12卷 23678-23693页

作者： Bi, Yuanguo Fu, Rao Jiang, Cunyu Zhang, Xiaoling Li, Fengyun Zhao, Liang Han, Guangjie Northeastern University School of Computer Science and Engineering Shenyang110169 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110169 China Shenyang University of Technology Institute of Artificial Intelligence Shenyang110870 China Shenyang Aerospace University School of Computer Science Shenyang110135 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

To enhance the reliability of rotating machinery, cross-domain fault diagnosis becomes vital for detecting faults under unknown operating conditions. However, multi-source domain imbalanced data present significant challenges, as divergent label distributions across domains cause complex domain-class shifts and degrade the performance of cross-domain fault diagnosis. Moreover, diagnostic models often struggle to learn features from minority classes due to label imbalance within each domain, which may degrade the performance in diagnosing these minority classes. To address these challenges, we propose a Dual-Reweighted Siamese Contrastive learning network (DRSC) for cross-domain fault diagnosis with multi-source domain imbalanced data. In DRSC, we design a Siamese feature extractor based on a wide-kernel convolutional neural network to capture short-term characteristics and leverage the convenience in extracting domain-invariant features. Subsequently, to alleviate domain-class shifts, we design a reweighted contrastive domain-class alignment mechanism that strategically pulls domain-class pairs together while pushing other health conditions away. Finally, to enable the diagnostic model to learn from minority health conditions, a reweighted health condition classifier is developed by assigning higher weights to the minority classes. Evaluation results on two public datasets illustrate DRSC outperforms comparison models in cross-domain fault diagnosis. © 2014 IEEE.

关键词： Convolutional neural networks

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Physics-Informed Neural Networks for Parametric Compressible Euler Equations

arXiv

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arXiv 2023年

作者： Wassing, Simon Langer, Stefan Bekemeyer, Philipp German Aerospace Center Institute for Aerodynamics and Flow Technology Center for Computer Applications in Aerospace Science and Engineering Lilienthalplatz 7 Braunschweig38108 Germany

The numerical approximation of solutions to the compressible Euler and Navier-Stokes equations is a crucial but challenging task with relevance in various fields of science and engineering. Recently, methods from deep learning have been successfully employed for solving partial differential equations by incorporating the equations into a loss function that is minimized during the training of a neural network. This approach yields a so-called physicsinformed neural network. It is not based upon classical discretizations, such as finite-volume or finite-element schemes, and can even address parametric problems in a straightforward manner. This has raised the question, whether physics-informed neural networks may be a viable alternative to conventional methods for computational fluid dynamics. In this article we introduce an adaptive artificial viscosity reduction procedure for physics-informed neural networks enabling approximate parametric solutions for forward problems governed by the stationary two-dimensional Euler equations in sub- and supersonic conditions. To the best of our knowledge, this is the first time that the concept of artificial viscosity in physics-informed neural networks is successfully applied to a complex system of conservation laws in more than one dimension. Moreover, we highlight the unique ability of this method to solve forward problems in a continuous parameter space. The presented methodology takes the next step of bringing physics-informed neural networks closer towards realistic compressible flow applications. © 2023, CC BY-NC-ND.

关键词： Deep learning

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Artificial Viscosity in Physics-Informed Neural Networks for Parametric Compressible Flows

SSRN

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SSRN 2023年

作者： Wassing, Simon Langer, Stefan Bekemeyer, Philipp German Aerospace Center Institute for Aerodynamics Flow Technology Center for Computer Applications in Aerospace Science and Engineering Lilienthalplatz 7 Braunschweig38108 Germany

The numerical approximation of solutions to the compressible Euler and Navier-stokes equations is a crucial but challenging task with relevance in various fields of science and engineering. Recently, methods from deep learning have been successfully employed for solving partial differential equations by incorporating the equations into a loss function that is minimized during the training of a neural network. This approach yields a so-called physics-informed neural network. It is not based upon classical discretizations, such as finite-volume or finite-element schemes, and can even address parametric problems in a straightforward manner. This has raised the question, whether physics-informed neural networks may be a viable alternative to conventional methods for computational fluid dynamics. In this article we propose a physics-informed neural network training procedures to approximate steady-state solutions of boundary-value problems for the compressible Euler equations. It turns out that the addition of artificial dissipation during the training process is important to avoid unphysical results. A method for reducing this additional numerical viscosity during the training is presented. Furthermore, we showcase how this approach can be combined with parametric boundary conditions. Our results highlight the appearance of unphysical results when solving compressible flows with physics-informed neural networks and offer a new approach to overcome this problem. © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Heterogeneity Modeling with Flexibility-based Graph Neural Networks 22

Heterogeneity Modeling with Flexibility-based Graph Neural N...

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22nd IEEE International Symposium on Parallel and Distributed Processing with applications, ISPA 2024

作者： Qin, Jisheng Tao, Tao Liu, Zijie Chen, Haibo Zhao, Shenghui Chuzhou University School of Computer and Information Engineering Anhui Chuzhou China Anhui University of Technology School of Computer Science and Technology Anhui Ma'anshan China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology China

ISBN: (纸本)9798331509712

Graph Neural Networks (GNNs) have demonstrated robust performance in various graph-based machine learning tasks, especially in homophilous graphs, where edges tend to connect nodes with the same label. However, achieving consistent performance on heterophilous graphs remains challenging. Several works have extended GNN architectures to address the distinct characteristics of heterogeneous graphs. However, these methods often need to enhance the flexibility of handling feature combinations for the central node and its neighbors. In this paper, we introduce a novel approach to modeling heterogeneity with Flexibility-based Graph Convolutional Networks (FGCN). FGCN consists of two primary components: first, it independently encodes the central node and its neighbors, fusing them to obtain an output representation. Second, it aggregates nodes with the same label from a feature and neighbor perspective, imposing constraints on the transformation during graph structure propagation. This approach enhances the flexibility of combining the central node and neighbor representations. We conducted extensive experiments on eight real-world datasets, demonstrating that FGCN outperforms state-of-the-art (SOTA) baseline models. © 2024 IEEE.

关键词： Convolutional neural networks

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An Improved Reversible Data Hiding Scheme Based on Referred Frequencies for a VQ Compressed Index 2

An Improved Reversible Data Hiding Scheme Based on Referred ...

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2nd International Conference on Machine Vision, Image Processing and Imaging Technology, MVIPIT 2024

作者： Hwang, Min-Shiang Fatima, Kanza Wang, Yu-Lun Chan, Chi-Shiang Wu, Chia-Chun Department of Computer Science & Information Engineering Fintech and Blockchain Research Center Asia University Taichung Taiwan Department of Computer Science & Information Engineering Asia University Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of M-Commerce and Multimedia Applications Asia University Taichung Taiwan Department of Computer Science and Information Engineering National Quemoy University Kinmen Taiwan

ISBN: (纸本)9798331543037

Nowadays, we usually compress images before uploading them to social media. However, images on social media can easily be copied, so embedding secret messages in compressed images has become increasingly popular. There are many compression methods, such as Huffman, VQ, ZIP, AMBTC, RAR, JPEG, etc. In this article, we propose an improved data hiding in VQ compression method to achieve better capacity and high quality. Experimental results show that our data-hiding approach is practical. ©2024 IEEE.

关键词： compressed image data hiding LSB matching reversible data hiding steganography vector quantization

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AceRoute: Adaptive Compute-Efficient FPGA Routing with Pluggable Intra-Connection Bidirectional Exploration 24

AceRoute: Adaptive Compute-Efficient FPGA Routing with Plugg...

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43rd International Conference on computer-Aided Design, ICCAD 2024

作者： Wei, Xinming Zhang, Ziyun Zou, Sunan Sun, Kaiwen Zhang, Jiahao Zhang, Jiaxi Fan, Ping Luo, Guojie School of Computer Science Peking University Beijing China DeePoly Technology Inc. Beijing China Undergraduate School of Electronics Engineering and Computer Science Peking University Beijing China Center for Energy-efficient Computing and Applications Peking University Beijing China

ISBN: (纸本)9798400710773

This paper introduces AceRoute, an adaptive compute-efficient FPGA router that tackles the long-standing issue of lengthy FPGA compilation times given complicated FPGA architectures and designs to synthesize. We thoroughly profile modern FPGA routing patterns and identify the runtime hotspot: routing bottleneck connections in congested designs. However, previous works on routing acceleration hardly target mitigating connection-wise routing difficulties by characterizing device resource expansions and shifting path-exploration modes of connections. In this work, we propose a bidirectional intra-connection routing paradigm for the first time, which efficiently navigates congested device regions by initiating searches from both source and sink nodes. This approach significantly outperforms traditional unidirectional exploration in congested conditions. Furthermore, for each connection to route, we develop an adaptive strategy to select the optimal search mode online between uni- and bi-directional based on the current congestion situation. Our approach is pluggable and versatile, allowing seamless integration into existing FPGA routers and providing instant speed improvements with merely several hundred lines of code. Evaluation on FPGA24 contest benchmarks shows that our router, powered by adaptive bidirectional search, achieves over 2.4× and 3.2× faster routing on average with a serial and a 2-thread parallel version, respectively, compared with established routing tools RWRoute and Vivado. Additionally, when integrated into typical partition-based inter-connection parallel routers, our approach overcomes their inherent load-balance problems and amplifies the speedup to RWRoute from 2.2× to up to 5×. © 2024 Copyright is held by the owner/author(s).

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